Domain Adaptation Transduction: An Algorithm for Autonomous Training with Applications to Activity Recognition Using Wearable Devices
Yang Li, Yuanyuan Bao, Wai Chen · 2018
With the emergence of the Internet-of-Things (IoT), we are witnessing rapid increases in the use of wearable devices, which are typically used to monitor human activities and may provide, by leveraging machine learning techniques, underlying contextual characteristics of the human wearers. Upon changes in the IoT environment, most machine learning algorithms need to be retrained-a process that needs large amount of labeled training data. In this paper, we focus on the situation where a new wearable device is added to an existing IoT system which has finite computation ability and memory space, and investigate how to improve the recognition accuracy with the newly added wearable device-without labeling any new instances. We propose a novel algorithm referred to as Domain Adaptation Transduction (DAT), for autonomously reconfiguring the wearable algorithms. Our experimental results on commonly-used benchmark datasets show that the proposed DAT algorithm yields better performance than existing methods when the labeled data is small.